Computational design of anti-cancer peptides tailored to target specific tumor markers.

Anti-cancer peptides (ACPs) are short peptides known for their ability to inhibit tumor cell proliferation, migration, and the formation of tumor blood vessels. In this study, we designed ACPs to target receptors often overexpressed in cancer using a systematic in silico approach. Three target receptors (CXCR1, DcR3, and OPG) were selected for their significant roles in cancer pathogenesis and tumor cell proliferation. Our peptide design strategy involved identifying interacting residues (IR) of these receptors, with their natural ligands serving as a reference for designing peptides specific to each receptor. The natural ligands of these receptors, including IL8 for CXCR1, TL1A for DcR3, and RANKL for OPG, were identified from the literature. Using the identified interacting residues (IR), we generated a peptide library through simple permutation and predicted the structure of each peptide. All peptides were analyzed using the web-based prediction server for Anticancer peptides, AntiCP. Docking simulations were then conducted to analyze the binding efficiencies of peptides with their respective target receptors, using VEGA ZZ and Chimera for interaction analysis. Our analysis identified HPKFIKELR as the interacting residues (IR) of CXCR-IL8. For DcR3, we utilized three domains from TL1A (TDSYPEP, TKEDKTF, LGLAFTK) as templates, along with two regions (SIKIPSS and PDQDATYP) from RANKL, to generate a library of peptide analogs. Subsequently, peptides for each receptor were shortlisted based on their predicted anticancer properties as determined by AntiCP and were subjected to docking analysis. After docking, peptides that exhibited the least binding energy were further analyzed for their detailed interaction with their respective receptors. Among these, peptides C9 (HPKFELY) and C7 (HPKFEWL) for CXCR1, peptides D6 (ADSYPQP) and D18 (AFSYPFP) for DcR3, and peptides P19 (PDTYPQDP) and p16 (PDQDATYP) for OPG, demonstrated the highest affinity and stronger interactions compared to the other peptides. Although in silico predictions indicated a favorable binding affinity of the designed peptides with target receptors, further experimental validation is essential to confirm their binding affinity, stability and pharmacokinetic characteristics.

Revolutionizing cancer care strategies: immunotherapy, gene therapy, and molecular targeted therapy.

Despite the availability of technological advances in traditional anti-cancer therapies, there is a need for more precise and targeted cancer treatment strategies. The wide-ranging shortfalls of conventional anticancer therapies such as systematic toxicity, compromised life quality, and limited to severe side effects are major areas of concern of conventional cancer treatment approaches. Owing to the expansion of knowledge and technological advancements in the field of cancer biology, more innovative and safe anti-cancerous approaches such as immune therapy, gene therapy and targeted therapy are rapidly evolving with the aim to address the limitations of conventional therapies. The concept of immunotherapy began with the capability of coley toxins to stimulate toll-like receptors of immune cells to provoke an immune response against cancers. With an in-depth understating of the molecular mechanisms of carcinogenesis and their relationship to disease prognosis, molecular targeted therapy approaches, that inhibit or stimulate specific cancer-promoting or cancer-inhibitory molecules respectively, have offered promising outcomes. In this review, we evaluate the achievement and challenges of these technically advanced therapies with the aim of presenting the overall progress and perspective of each approach.

A concise overview of advancements in ultrasensitive biosensor development.

Electrochemical biosensing has evolved as a diverse and potent method for detecting and analyzing biological entities ranging from tiny molecules to large macromolecules. Electrochemical biosensors are a desirable option in a variety of industries, including healthcare, environmental monitoring, and food safety, due to significant advancements in sensitivity, selectivity, and portability brought about by the integration of electrochemical techniques with nanomaterials, bio-recognition components, and microfluidics. In this review, we discussed the realm of electrochemical sensors, investigating and contrasting the diverse strategies that have been harnessed to push the boundaries of the limit of detection and achieve miniaturization. Furthermore, we assessed distinct electrochemical sensing methods employed in detection such as potentiometers, amperometers, conductometers, colorimeters, transistors, and electrical impedance spectroscopy to gauge their performance in various contexts. This article offers a panoramic view of strategies aimed at augmenting the limit of detection (LOD) of electrochemical sensors. The role of nanomaterials in shaping the capabilities of these sensors is examined in detail, accompanied by insights into the chemical modifications that enhance their functionality. Furthermore, our work not only offers a comprehensive strategic framework but also delineates the advanced methodologies employed in the development of electrochemical biosensors. This equips researchers with the knowledge required to develop more accurate and efficient detection technologies.

Memorability-based multimedia analytics for robotic interestingness prediction system using trimmed Q-learning algorithm.

Mobile robots are increasingly employed in today's environment. Perceiving the environment to perform a task plays a major role in the robots. The service robots are wisely employed in the fully (or) partially known user's environment. The exploration and exploitation of the unknown environment is a tedious task. This paper introduces a novel Trimmed Q-learning algorithm to predict interesting scenes via efficient memorability-oriented robotic behavioral scene activity training. The training process involves three stages: online learning and short-term and long-term learning modules. It is helpful for autonomous exploration and making wiser decisions about the environment. A simplified three-stage learning framework is introduced to train and predict interesting scenes using memorability. A proficient visual memory schema (VMS) is designed to tune the learning parameters. A role-based profile arrangement is made to explore the unknown environment for a long-term learning process. The online and short-term learning frameworks are designed using a novel Trimmed Q-learning algorithm. The underestimated bias in robotic actions must be minimized by introducing a refined set of practical candidate actions. Finally, the recalling ability of each learning module is estimated to predict the interesting scenes. Experiments conducted on public datasets, SubT, and SUN databases demonstrate the proposed technique's efficacy. The proposed framework has yielded better memorability scores in short-term and online learning at 72.84% and in long-term learning at 68.63%.

The NWRD Dataset: An Open-Source Annotated Segmentation Dataset of Diseased Wheat Crop.

Wheat stripe rust disease (WRD) is extremely detrimental to wheat crop health, and it severely affects the crop yield, increasing the risk of food insecurity. Manual inspection by trained personnel is carried out to inspect the disease spread and extent of damage to wheat fields. However, this is quite inefficient, time-consuming, and laborious, owing to the large area of wheat plantations. Artificial intelligence (AI) and deep learning (DL) offer efficient and accurate solutions to such real-world problems. By analyzing large amounts of data, AI algorithms can identify patterns that are difficult for humans to detect, enabling early disease detection and prevention. However, deep learning models are data-driven, and scarcity of data related to specific crop diseases is one major hindrance in developing models. To overcome this limitation, in this work, we introduce an annotated real-world semantic segmentation dataset named the NUST Wheat Rust Disease (NWRD) dataset. Multileaf images from wheat fields under various illumination conditions with complex backgrounds were collected, preprocessed, and manually annotated to construct a segmentation dataset specific to wheat stripe rust disease. Classification of WRD into different types and categories is a task that has been solved in the literature; however, semantic segmentation of wheat crops to identify the specific areas of plants and leaves affected by the disease remains a challenge. For this reason, in this work, we target semantic segmentation of WRD to estimate the extent of disease spread in wheat fields. Sections of fields where the disease is prevalent need to be segmented to ensure that the sick plants are quarantined and remedial actions are taken. This will consequently limit the use of harmful fungicides only on the targeted disease area instead of the majority of wheat fields, promoting environmentally friendly and sustainable farming solutions. Owing to the complexity of the proposed NWRD segmentation dataset, in our experiments, promising results were obtained using the UNet semantic segmentation model and the proposed adaptive patching with feedback (APF) technique, which produced a precision of 0.506, recall of 0.624, and F1 score of 0.557 for the rust class.

Clinical relevance of circulating non-coding RNAs in metabolic diseases: Emphasis on obesity, diabetes, cardiovascular diseases and metabolic syndrome.

Non-coding RNAs (ncRNAs) participate in the regulation of several cellular processes including transcription, RNA processing and genome rearrangement. The aberrant expression of ncRNAs is associated with several pathological conditions. In this review, we focused on recent information to elucidate the role of various regulatory ncRNAs i.e., micro RNAs (miRNAs), circular RNAs (circRNAs) and long-chain non-coding RNAs (lncRNAs), in metabolic diseases, e.g., obesity, diabetes mellitus (DM), cardiovascular diseases (CVD) and metabolic syndrome (MetS). The mechanisms by which ncRNAs participated in disease pathophysiology were also highlighted. miRNAs regulate the expression of genes at transcriptional and translational levels. circRNAs modulate the regulation of gene expression via miRNA sponging activity, interacting with RNA binding protein and polymerase II transcription regulation. lncRNAs regulate the expression of genes by acting as a protein decoy, miRNA sponging, miRNA host gene, binding to miRNA response elements (MRE) and the recruitment of transcriptional element or chromatin modifiers. We examined the role of ncRNAs in the disease pathogenesis and their potential role as molecular markers for diagnosis, prognosis and therapeutic targets. We showed the involvement of ncRNAs in the onset of obesity and its progression to MetS and CVD. miRNA-192, miRNA-122, and miRNA-221 were dysregulated in all these metabolic diseases. Other ncRNAs, implicated in at least three diseases include miRNA-15a, miRNA-26, miRNA-27a, miRNA-320, and miRNA-375. Dysregulation of ncRNAs increased the risk of development of DM and MetS and its progression to CVD in obese individuals. Hence, these molecules are potential targets to arrest or delay the progression of metabolic diseases.

Holistic expression of miR-17-92 cluster in obesity, kidney diseases, cardiovascular diseases, and diabetes.

miR-17-92 cluster encodes six micro RNAs (miRNAs) and plays a crucial role in the regulation of various cellular processes. Aberrant expression of this cluster may result in the onset of several diseases. Initially, the role of miR-17-92 cluster in tumorigenesis was discovered but recent research has also uncovered its role in other diseases. Members of the cluster may serve as potential biomarkers in the prognosis, diagnosis, and treatment of several diseases and their complications. In this article, we have reviewed the recent research carried out on the expression pattern of miR-17-92 cluster in non-communicable diseases i.e., obesity, cardiovascular diseases (CVD), kidney diseases (KD) and diabetes mellitus (DM). We examined miR-17-92 role in pathological processes and their potential importance as biomarkers. Each member of the cluster miR-17-92 was upregulated in obesity. miR-18a, miR-19b-3p, miR20a, and miR92a were significantly upregulated in CVD. An equal fraction of the cluster was dysregulated (upregulated and downregulated) in diabetes; however, miR-17-92 was downregulated in most studies on CKD.

MDM2- an indispensable player in tumorigenesis.

Murine double minute 2 (MDM2) is a well-recognized molecule for its oncogenic potential. Since its identification, various cancer-promoting roles of MDM2 such as growth stimulation, sustained angiogenesis, metabolic reprogramming, apoptosis evasion, metastasis, and immunosuppression have been established. Alterations in the expression levels of MDM2 occur in multiple types of cancers resulting in uncontrolled proliferation. The cellular processes are modulated by MDM2 through transcription, post-translational modifications, protein degradation, binding to cofactors, and subcellular localization. In this review, we discuss the precise role of deregulated MDM2 levels in modulating cellular functions to promote cancer growth. Moreover, we also briefly discuss the role of MDM2 in inducing resistance against anti-cancerous therapies thus limiting the benefits of cancerous treatment.

High level of lactate dehydrogenase and ischaemia-reperfusion injury regulate the multiple organ dysfunction in patients with COVID-19.

BACKGROUND: Multiple organ damage has been observed in patients with COVID-19, but the exact pathway is not known. Vital organs of the human body may get affected after replication of SARS-CoV-2, including the lungs, heart, kidneys, liver and brain. It triggers severe inflammation and impairs the function of two or more organ systems. Ischaemia-reperfusion (IR) injury is a phenomenon that can have disastrous effects on the human body. METHODS: In this study, we analysed the laboratory data of 7052 hospitalised patients with COVID-19 including lactate dehydrogenase (LDH). A total of 66.4% patients were men and 33.6% were women, which indicated gender difference as a prominent factor to be considered. RESULTS: Our data showed high levels of inflammation and elevated markers of tissue injury from multiple organs C reactive protein, white blood cell count, alanine transaminase, aspartate aminotransferase and LDH. The number of red blood cells, haemoglobin concentration and haematocrit were lower than normal which indicated a reduction in oxygen supply and anaemia. CONCLUSION: On the basis of these results, we proposed a model linking IR injury to multiple organ damage by SARS-CoV-2. COVID-19 may cause a reduction in oxygen towards an organ, which leads to IR injury.

Road Feature Detection for Advance Driver Assistance System Using Deep Learning.

Hundreds of people are injured or killed in road accidents. These accidents are caused by several intrinsic and extrinsic factors, including the attentiveness of the driver towards the road and its associated features. These features include approaching vehicles, pedestrians, and static fixtures, such as road lanes and traffic signs. If a driver is made aware of these features in a timely manner, a huge chunk of these accidents can be avoided. This study proposes a computer vision-based solution for detecting and recognizing traffic types and signs to help drivers pave the door for self-driving cars. A real-world roadside dataset was collected under varying lighting and road conditions, and individual frames were annotated. Two deep learning models, YOLOv7 and Faster RCNN, were trained on this custom-collected dataset to detect the aforementioned road features. The models produced mean Average Precision (mAP) scores of 87.20% and 75.64%, respectively, along with class accuracies of over 98.80%; all of these were state-of-the-art. The proposed model provides an excellent benchmark to build on to help improve traffic situations and enable future technological advances, such as Advance Driver Assistance System (ADAS) and self-driving cars.

Computational Analysis Reveals Distinctive Interaction of miRNAs with Target Genes in the Pathogenesis of Chronic Kidney Disease.

The regulation of genes is crucial for maintaining a healthy intracellular environment, and any dysregulation of gene expression leads to several pathological complications. It is known that many diseases, including kidney diseases, are regulated by miRNAs. However, the data on the use of miRNAs as biomarkers for the diagnosis and treatment of chronic kidney disease (CKD) are not conclusive. The purpose of this study was to elucidate the potential of miRNAs as an efficient biomarker for the detection and treatment of CKD at its early stages. Gene expression profiling data were acquired from the Gene Expression Omnibus (GEO) and differentially expressed genes (DEGs) were identified. miRNAs directly associated with CKD were obtained from an extensive literature search. Network illustration of miRNAs and their projected target differentially expressed genes (tDEGs) was accomplished, followed by functional enrichment analysis. hsa-miR-1-3p, hsa-miR-206, hsa-miR-494 and hsa-miR-577 exhibited a strong association with CKD through the regulation of genes involved in signal transduction, cell proliferation, the regulation of transcription and apoptotic process. All these miRNAs have shown significant contributions to the inflammatory response and the processes which eventually lead to the pathogenesis of CKD. The in silico approach used in this research represents a comprehensive analysis of identified miRNAs and their target genes for the identification of molecular markers of disease processes. The outcomes of the study recommend further efforts for developing miRNA biomarkers set for the early diagnosis of CKD.

Competing Endogenous RNA Regulatory Networks of hsa_circ_0126672 in Pathophysiology of Coronary Heart Disease.

Coronary heart disease (CHD) is a global health concern, and its molecular origin is not fully elucidated. Dysregulation of ncRNAs has been linked to many metabolic and infectious diseases. This study aimed to explore the role of circRNAs in the pathogenesis of CHD and predicted a candidate circRNA that could be targeted for therapeutic approaches to the disease. circRNAs associated with CHD were identified and CHD gene expression profiles were obtained, and analyzed with GEO2R. In addition, differentially expressed miRNA target genes (miR-DEGs) were identified and subjected to functional enrichment analysis. Networks of circRNA/miRNA/mRNA and the miRNA/affected pathways were constructed. Furthermore, a miRNA/mRNA homology study was performed. We identified that hsa_circ_0126672 was strongly associated with the CHD pathology by competing for endogenous RNA (ceRNA) mechanisms. hsa_circ_0126672 characteristically sponges miR-145-5p, miR-186-5p, miR-548c-3p, miR-7-5p, miR-495-3p, miR-203a-3p, and miR-21. Up-regulation of has_circ_0126672 affected various CHD-related cellular functions, such as atherosclerosis, JAK/STAT, and Apelin signaling pathways. Our results also revealed a perfect and stable interaction for the hybrid of miR-145-5p with NOS1 and RPS6KB1. Finally, miR-145-5p had the highest degree of interaction with the validated small molecules. Henchashsa_circ_0126672 and target miRNAs, notably miR-145-5p, could be good candidates for the diagnosis and therapeutic approaches to CHD.

Genetic polymorphism of Β-casein gene and its association with milk production and composition in Azi-Kheli buffalo.

The aim of this study was to find out the genetic polymorphism in ß-casein gene CSN2 in Azi-Kheli buffaloes found in district Swat. Blood samples from 250 buffaloes were collected and processed in lab for sequencing to see the genetic polymorphism in CSN2 gene on 67 position of exon7. The ß-casein is a milk second abundant protein having some variants, wherein A1 and A2 are the most common. After performing sequence analysis, it was found that Azi-Kheli buffaloes were homozygous for only A2 type variant. The amino acid change (proline to histadine) on 67 position of exon 7 was not found; however, three other novel SNPs at loci g.20545A > G, g.20570G > A, and g.20693C > A were identified in the study. Amino acid change due to SNPs were found as SNP1, valine > proline; SNP2, leucin > phenylalanine; and SNP3, threonine > valine. Allelic and genotypic frequencies' analysis exhibited that all three SNPs were following the Hardy-Weinberg equilibrium (HWE: P < 0.05). All the three SNPs showed medium PIC value and gene heterozygosity. The SNPs located on different position of exon 7 of CSN2 gene exhibited associations with some of the performance traits and milk composition. Higher daily milk yield of 9.86 ± 0.43 L and the peak milk yield of 13.80 ± 0.60 L were found in response to SNP3 followed by SNP2 and SNP1. The percentage of milk fat and protein was found significantly higher (P ≤ 0.05) in relation to SNP3 followed by SNP2 and SNP1 given as 7.88 ± 0.41, 7.48 ± 0.33, and 7.15 ± 0.48 for fat% and 4.00 ± 0.15, 3.73 ± 0.10 and 3.40 ± 0.10 for protein%. It was concluded that Azi-Kheli buffalo milk contains A2 genetic variant along with other useful novel variants indicating quality milk for human health. Genotypes of SNP3 should be given preference in selection both in indices and nucleotide polymorphism.

Antibacterial Activity of Dental Composite with Ciprofloxacin Loaded Silver Nanoparticles.

Resin composites have been widely used in dental restoration. However, polymerization shrinkage and resultant bacterial microleakage are major limitations that may lead to secondary caries. To overcome this, a new type of antibacterial resin composite containing ciprofloxacin-loaded silver nanoparticles (CIP-AgNPs) were synthesized. The chemical reduction approach successfully produced CIP-AgNPs, as demonstrated by FTIR, zeta potential, scanning electron microscopy, and ultraviolet-visible (UV-vis) spectroscopy. CIP-AgNPs were added to resin composites and the antibacterial activity of the dental composite discs were realized against Enterococcus faecalis, Streptococcus mutans, and the Saliva microcosm. The biocompatibility of modified resin composites was assessed and mechanical testing of modified dental composites was also performed. The results indicated that the antibacterial activity and compressive strength of resin composites containing CIP-AgNPs were enhanced compared to the control group. They were also biocompatible when compared to resin composites containing AgNPs. In short, these results established strong ground application for CIP-AgNP-modified dental composite resins.

Two hours method for RNA and DNA co-extraction from blood of coronary artery disease patients: Fast, simple and economical technique.

Objectives: Extraction of DNA and RNA is the first step in genomics and transcriptomics studies. Phenol-chloroform method for DNA extraction has been the widely used method. However, this method is relatively expensive and time-consuming. The objective of the present study was to validate a cost and time-effective protocol that will reduce the burden of molecular biology-based research and make a difference in laboratories with limited resources. Methods: A comparative study was conducted at Syed Qamer Alam Research Laboratory, Shifa College of Medicine; from February, 2021 to August, 2021. TRIzol™ method was used to extract RNA from blood samples of coronary artery disease patients and remnant was used to extract DNA. The quantity, purity and integrity of the extracted DNA by both methods (TRIzol and phenol-chloroform) was examined. PCR product amplification was performed with thrombomodulin (THBD) gene to validate the characteristic of the extracted DNA and its efficiency for downstream experiments. Results: The DNA yield in the TRIzol™ method was three-fold higher than phenol chloroform method. Both methods showed intact genomic DNA on the agarose gel, and extracted DNA was efficient for PCR amplification. Conclusion: The TRIzol™ method for RNA and DNA co-extraction is fast, simple and economical technique. So, it can be adopted for routine molecular biology analyses in limited resources setup.

Free Fatty Acid Overload Targets Mitochondria: Gene Expression Analysis of Palmitic Acid-Treated Endothelial Cells.

Lipotoxicity is known to cause cellular dysfunction and death in non-adipose tissue. A major cause of lipotoxicity is the accumulation of saturated free fatty acids (FFA). Palmitic acid (PA) is the most common saturated fatty acid found in the human body. Endothelial cells form the blood vessels and are the first non-adipose cells to encounter FFA in the bloodstream. FFA overload has a direct impact on metabolism, which is evident through the changes occurring in mitochondria. To study these changes, the PA-treated human coronary artery endothelial cell (HCAEC) dataset was obtained from the Gene Expression Omnibus (GEO), and it was analyzed to obtain differentially expressed genes (DEGs) from the nucleus and mitochondria. Functional and pathway enrichment analyses were performed on DEGs. Results showed that nuclear and mitochondrial DEGs were implicated in several processes, e.g., reactive oxygen species (ROS) production, mitochondrial fusion and fission, Ca2+ sequestering, membrane transport, the electron transport chain and the process of apoptosis. To better understand the role of FFA in endothelial cell damage, these DEGs can lead to future experiments based on these findings.

EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM.

The constantly evolving human-machine interaction and advancement in sociotechnical systems have made it essential to analyze vital human factors such as mental workload, vigilance, fatigue, and stress by monitoring brain states for optimum performance and human safety. Similarly, brain signals have become paramount for rehabilitation and assistive purposes in fields such as brain-computer interface (BCI) and closed-loop neuromodulation for neurological disorders and motor disabilities. The complexity, non-stationary nature, and low signal-to-noise ratio of brain signals pose significant challenges for researchers to design robust and reliable BCI systems to accurately detect meaningful changes in brain states outside the laboratory environment. Different neuroimaging modalities are used in hybrid settings to enhance accuracy, increase control commands, and decrease the time required for brain activity detection. Functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) measure the hemodynamic and electrical activity of the brain with a good spatial and temporal resolution, respectively. However, in hybrid settings, where both modalities enhance the output performance of BCI, their data compatibility due to the huge discrepancy between their sampling rate and the number of channels remains a challenge for real-time BCI applications. Traditional methods, such as downsampling and channel selection, result in important information loss while making both modalities compatible. In this study, we present a novel recurrence plot (RP)-based time-distributed convolutional neural network and long short-term memory (CNN-LSTM) algorithm for the integrated classification of fNIRS EEG for hybrid BCI applications. The acquired brain signals are first projected into a non-linear dimension with RPs and fed into the CNN to extract essential features without performing any downsampling. Then, LSTM is used to learn the chronological features and time-dependence relation to detect brain activity. The average accuracies achieved with the proposed model were 78.44% for fNIRS, 86.24% for EEG, and 88.41% for hybrid EEG-fNIRS BCI. Moreover, the maximum accuracies achieved were 85.9, 88.1, and 92.4%, respectively. The results confirm the viability of the RP-based deep-learning algorithm for successful BCI systems.

Evaluation of the putative lymphoma-associated point mutation D427H in the STAT3 transcription factor.

BACKGROUND: Signal transducer and activator of transcription 3 (STAT3) is an oncogenic transcription factor that promotes cell proliferation and immunomodulation in untransformed cells and maintains stemness of transformed cells, facilitating invasion and metastasis. Numerous point mutations in the STAT3 protein have been identified that drive malignancy in various tumor entities. The missense mutation D427H localized in the STAT3 DNA-binding domain has been previously reported in patients with NK/T cell lymphomas. To assess the biological activity of this missense mutation, we compared the STAT3-D427H mutant to wild-type (WT) protein as well as the known hyper-active mutant F174A. RESULTS: Although previously reported as an activating mutation, the STAT3-D427H mutant neither showed elevated cytokine-induced tyrosine phosphorylation nor altered nuclear accumulation, as compared to the WT protein. However, the D427H mutant displayed enhanced binding to STAT-specific DNA-binding sites but a reduced sequence specificity and dissociation rate from DNA, which was demonstrated by electrophoretic mobility shift assays. This observation is consistent with the phenotype of the homologous E421K mutation in the STAT1 protein, which also displayed enhanced binding to DNA but lacked a corresponding increase in transcriptional activity. CONCLUSIONS: Based on our data, it is unlikely that the D427H missense mutation in the STAT3 protein possesses an oncogenic potential beyond the WT molecule.